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Learning goal hierarchies from structured observations and expert annotations

MACHINE LEARNING 2006年第1-3期64卷 263-287页

作者： Koenik, Tolga Laird, John E. Stanford Univ Ctr Study Language & Informat Computat Learning Lab Stanford CA 94305 USA Univ Michigan Dept Elect Engn & Comp Sci Artificial Intelligence Lab Ann Arbor MI 48109 USA

We describe a relational learning by observation framework that automatically creates cognitive agent programs that model expert task performance in complex dynamic domains. Our framework uses observed behavior and goal annotations of an expert as the primary input, interprets them in the context of background knowledge, and returns an agent program that behaves similar to the expert. We map the problem of creating an agent program on to multiple learning problems that can be represented in a "supervised concept learning" setting. the acquired procedural knowledge is partitioned into a hierarchy of goals and represented with first order rules. Using an inductive logic programming (ILP) learning component allows our framework to naturally combine structured behavior observations, parametric and hierarchical goal annotations, and complex background knowledge. To deal with the large domains we consider, we have developed an efficient mechanism for storing and retrieving structured behavior data. We have tested our approach using artificially created examples and behavior observation traces generated by AI agents. We evaluate the learned rules by comparing them to hand-coded rules.

关键词： relational learning by observation relational learning inductive logic programming (ILP) behavioral cloning cognitive agent architectures

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CIC^∧:: Type-based termination of recursive definitions in the calculus of inductive constructions

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13th international conference on logic for programming, Artificial Intelligence, and Reasoning

作者： Barthe, Gilles Gregoire, Benjamin Pastawski, Fernando INRIA Sophia Antipolis France Univ Nacl Cordoba FaMAF RA-5000 Cordoba Argentina

ISBN: (纸本)3540482814

Sized types provides a type-based mechanism to enforce termination of recursive definitions in typed A-calculi. Previous work has provided strong indications that type-based termination provides an appropriate foundation for proof assistants based on type theory;however, most work to date has been confined to non-dependent type systems. In this article, we introduce a variant of the Calculus of inductive Constructions with sized types and study its meta theoretical properties: subject reduction, normalization, and thus consistency and decidability of type-checking and of size-inference. A prototype implementation has been developed alongside case studies.

关键词： Computational methods

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inductive decidability using implicit induction

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13th international conference on logic for programming, Artificial Intelligence, and Reasoning

作者： Falke, Stephan Kapur, Deepak Univ New Mexico Dept Comp Sci Albuquerque NM 87131 USA

ISBN: (纸本)3540482814

Decision procedures are widely used in automated reasoning tools in order to reason about data structures. In applications, many conjectures fall outside the theory handled by a decision procedure. Often, reasoning about user-defined functions on those data structures is needed. For this, inductive reasoning has to be employed. In this work, classes of function definitions and conjectures are identified for which inductive validity can be automatically decided using implicit induction methods and decision procedures for an underlying theory. the class of equational conjectures considered in this paper significantly extends the results of Kapur & Subramaniam (CADE, 2000) [15], which were obtained using explicit induction schemes. Firstly, nonlinear conjectures can be decided automatically. Secondly, function definitions can use other defined functions in their definitions, thus allowing mutually recursive functions and decidable conjectures about them. thirdly, conjectures can have general terms from the decidable theory on inductive positions. these contributions are crucial for successfully integrating inductive reasoning into decision procedures, thus enabling their use in push-button mode in applications including verification and program analysis.

关键词： logic programming

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Mathematical applications of inductive logic programming

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MACHINE LEARNING 2006年第1-3期64卷 25-64页

作者： Colton, Simon Muggleton, Stephen Univ London Imperial Coll Sci Technol & Med Dept Comp Computat Bioinformat Lab London SW7 2AZ England

the application of inductive logic programming to scientific datasets has been highly successful. Such applications have led to breakthroughs in the domain of interest and have driven the development of ILP systems. the application of AI techniques to mathematical discovery tasks, however, has largely involved computer algebra systems and theorem provers rather than machine learning systems. We discuss here the application of the HR and Progol machine learning programs to discovery tasks in mathematics. While Progol is an established ILP system, HR has historically not been described as an ILP system. However, many applications of HR have required the production of first order hypotheses given data expressed in a Prolog-style manner, and the core functionality of HR can be expressed in ILP terminology. In Colton (2003), we presented the first partial description of HR as an ILP system, and we build on this work to provide a full description here. HR performs a novel ILP routine called Automated theory Formation, which combines inductive and deductive reasoning to form clausal theories consisting of classification rules and association rules. HR generates definitions using a set of production rules, interprets the definitions as classification rules, then uses the success sets of the definitions to induce hypotheses from which it extracts association rules. It uses third party theorem provers and model generators to check whether the association rules are entailed by a set of user supplied axioms. HR has been applied successfully to a number of predictive, descriptive and subgroup discovery tasks in domains of pure mathematics. We survey various applications of HR which have led to it producing number theory results worthy of journal publication, graph theory results rivalling those of the highly successful Graffiti program and algebraic results leading to novel classification theorems. To further promote mathematics as a challenge domain for ILP systems, we present

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Complexity parameters for first order classes

Complexity parameters for first order classes

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13th international conference on inductive logic programming

作者： Arias, Marta Khardon, Roni Columbia Univ Ctr Computat Learning Syst New York NY 10115 USA Tufts Univ Dept Comp Sci Medford MA 02155 USA

ISBN: (纸本)9783540399179

We study several complexity parameters for first order formulas and their suitability for first order learning models. We show that the standard notion of size is not captured by sets of parameters that are used in the literature and thus they cannot give a complete characterization in terms of learnability with polynomial resources. We then identify an alternative notion of size and a simple set of parameters that are useful for first order Horn Expressions. these parameters are the number of clauses in the expression, the maximum number of distinct terms in a clause, and the maximum number of literals in a clause. Matching lower bounds derived using the Vapnik Chervonenkis dimension complete the picture showing that these parameters are indeed crucial.

关键词： inductive logic programming learning theory first-order logic VC-dimension query learning

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Quantitative pharmacophore models with inductive logic programming

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MACHINE LEARNING 2006年第1-3期64卷 65-90页

作者： Srinivasan, Ashwin Page, David Camacho, Rui King, Ross Indian Inst Technol IBM India Res Lab New Delhi India Univ Madison Dept Biostat Madison WI USA LIACC CIUP P-4150 Oporto Portugal Univ Wales Dept Comp Sci Aberystwyth Dyfed Wales

three-dimensional models, or pharmacophores, describing Euclidean constraints on the location on small molecules of functional groups (like hydrophobic groups, hydrogen acceptors and donors, etc.), are often used in drug design to describe the medicinal activity of potential drugs (or 'ligands'). this medicinal activity is produced by interaction of the functional groups on the ligand with a binding site on a target protein. In identifying structure-activity relations of this kind there are three principal issues: (1) It is often difficult to "align" the ligands in order to identify common structural properties that may be responsible for activity;(2) Ligands in solution can adopt different shapes (or 'conformations') arising from torsional rotations about bonds. the 3-D molecular substructure is typically sought on one or more low-energy conformers;and (3) Pharmacophore models must, ideally, predict medicinal activity on some quantitative scale. It has been shown that the logical representation adopted by inductive logic programming (ILP) naturally resolves many of the difficulties associated with the alignment and multi-conformation issues. However, the predictions of models constructed by ILP have hitherto only been nominal, predicting medicinal activity to be present or absent. In this paper, we investigate the construction of two kinds of quantitative pharmacophoric models with ILP: (a) Models that predict the probability that a ligand is "active";and (b) Models that predict the actual medicinal activity of a ligand. Quantitative predictions are obtained by the utilising the following statistical procedures as background knowledge: logistic regression and naive Bayes, for probability prediction;linear and kernel regression, for activity prediction. the multi-conformation issue and, more generally, the relational representation used by ILP results in some special difficulties in the use of any statistical procedure. We present the principal issues and some solut

关键词： pharmacophore models ILP statistical relational learning

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Gleaner: Creating ensembles of first-order clauses to improve recall-precision curves

Gleaner: Creating ensembles of first-order clauses to improv...

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13th international conference on inductive logic programming

作者： Goadrich, Mark Oliphant, Louis Shavlik, Jude Univ Wisconsin Dept Biostat & Med Informat Dept Comp Sci Madison WI 53706 USA

Many domains in the field of inductive logic programming (ILP) involve highly unbalanced data. A common way to measure performance in these domains is to use precision and recall instead of simply using accuracy. the goal of our research is to find new approaches within ILP particularly suited for large, highly-skewed domains. We propose Gleaner, a randomized search method that collects good clauses from a broad spectrum of points along the recall dimension in recall-precision curves and employs an "at least L of these K clauses" thresholding method to combine sets of selected clauses. Our research focuses on Multi-Slot Information Extraction (IE), a task that typically involves many more negative examples than positive examples. We formulate this problem into a relational domain, using two large testbeds involving the extraction of important relations from the abstracts of biomedical journal articles. We compare Gleaner to ensembles of standard theories learned by Aleph, finding that Gleaner produces comparable testset results in a fraction of the training time.

关键词： inductive logic programming ensembles recall-precision curves biomedical information extraction

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Learning goal hierarchies from structured observations and expert annotations

Learning goal hierarchies from structured observations and e...

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13th international conference on inductive logic programming

关键词： relational learning by observation relational learning inductive logic programming (ILP) behavioral cloning cognitive agent architectures

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Mathematical applications of inductive logic programming

Mathematical applications of inductive logic programming

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13th international conference on inductive logic programming

作者： Colton, Simon Muggleton, Stephen Univ London Imperial Coll Sci Technol & Med Dept Comp Computat Bioinformat Lab London SW7 2AZ England

关键词： logic programming

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Unifying logic, topology and learning in Parametric ogic

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thEORETICAL COMPUTER SCIENCE 2006年第1期350卷 103-124页

作者： Martin, É Sharma, A Stephan, F Univ New S Wales Sch Engn & Comp Sci Sydney NSW 2052 Australia Univ New S Wales Natl ICT Australia Sydney NSW 2052 Australia Queensland Univ Technol Div Res & Commercialisat Brisbane Qld 4001 Australia Natl Univ Singapore Sch Comp Singapore 117543 Singapore Natl Univ Singapore Dept Math Singapore 117543 Singapore

Many connections have been established between learning and logic, or learning and topology, or logic and topology. Still, the connections are not at the heart of these fields. Each of them is fairly independent of the others when attention is restricted to basic notions and main results. We show that connections can actually be made at a fundamental level, and result in a logic with parameters that needs topological notions for its early developments, and notions from learning theory for interpretation and applicability. One of the key properties of first-order logic is that the classical notion of logical consequence is compact. We generalize the notion of logical consequence, and we generalize compactness to beta-weak compactness where beta is an ordinal. the effect is to stratify the set of generalized logical consequences of a theory into levels, and levels into layers. Deduction corresponds to the lower layer of the first level above the underlying theory, learning with less than beta mind changes to layer beta of the first level, and learning in the limit to the first layer of the second level. Refinements of Borel-like hierarchies provide the topological tools needed to develop the framework. (c) 2005 Elsevier B.V. All rights reserved.

关键词： inductive logic inductive inference Borel and difference hierarchies weak forms of compactness

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